Loss of Prohibitin Membrane Scaffolds Impairs Mitochondrial Architecture and Leads to Tau Hyperphosphorylation and Neurodegeneration

Fusion and fission of mitochondria maintain the functional integrity of mitochondria and protect against neurodegeneration, but how mitochondrial dysfunctions trigger neuronal loss remains ill-defined. Prohibitins form large ring complexes in the inner membrane that are composed of PHB1 and PHB2 subunits and are thought to function as membrane scaffolds. In Caenorhabditis elegans, prohibitin genes affect aging by moderating fat metabolism and energy production. Knockdown experiments in mammalian cells link the function of prohibitins to membrane fusion, as they were found to stabilize the dynamin-like GTPase OPA1 (optic atrophy 1), which mediates mitochondrial inner membrane fusion and cristae morphogenesis. Mutations in OPA1 are associated with dominant optic atrophy characterized by the progressive loss of retinal ganglion cells, highlighting the importance of OPA1 function in neurons. Here, we show that neuron-specific inactivation of Phb2 in the mouse forebrain causes extensive neurodegeneration associated with behavioral impairments and cognitive deficiencies. We observe early onset tau hyperphosphorylation and filament formation in the hippocampus, demonstrating a direct link between mitochondrial defects and tau pathology. Loss of PHB2 impairs the stability of OPA1, affects mitochondrial ultrastructure, and induces the perinuclear clustering of mitochondria in hippocampal neurons. A destabilization of the mitochondrial genome and respiratory deficiencies manifest in aged neurons only, while the appearance of mitochondrial morphology defects correlates with tau hyperphosphorylation in the absence of PHB2. These results establish an essential role of prohibitin complexes for neuronal survival in vivo and demonstrate that OPA1 stability, mitochondrial fusion, and the maintenance of the mitochondrial genome in neurons depend on these scaffolding proteins. Moreover, our findings establish prohibitin-deficient mice as a novel genetic model for tau pathologies caused by a dysfunction of mitochondria and raise the possibility that tau pathologies are associated with other neurodegenerative disorders caused by deficiencies in mitochondrial dynamics.     

FoxP3+ Regulatory T Cells Determine Disease Severity in Rodent Models of Inflammatory Neuropathies

Inflammatory neuropathies represent disabling human autoimmune disorders with considerable disease variability. Animal models provide insights into defined aspects of their disease pathogenesis. Forkhead box P3 (FoxP3)+ regulatory T lymphocytes (Treg) are anti-inflammatory cells that maintain immune tolerance and counteract tissue damage in a variety of immune-mediated disorders. Dysfunction or a reduced frequency of Tregs have been associated with different human autoimmune disorders. We here analyzed the functional relevance of Tregs in determining disease manifestation and severity in murine models of autoimmune neuropathies. We took advantage of the DEREG mouse system allowing depletion of Treg with high specificity as well as anti-CD25 directed antibodies to deplete Tregs in mice in actively induced experimental autoimmune neuritis (EAN). Furthermore antibody-depletion was performed in an adoptive transfer model of chronic neuritis. Early Treg depletion increased clinical EAN severity both in active and adoptive transfer chronic neuritis. This was accompanied by increased proliferation of myelin specific T cells and histological signs of peripheral nerve inflammation. Late stage Treg depletion after initial disease manifestation however did not exacerbate inflammatory neuropathy symptoms further. We conclude that Tregs determine disease severity in experimental autoimmune neuropathies during the initial priming phase, but have no major disease modifying function after disease manifestation. Potential future therapeutic approaches targeting Tregs should thus be performed early in inflammatory neuropathies.     

CLIP-170 tracks growing microtubule ends by dynamically recognizing composite EB1/tubulin-binding sites

The microtubule cytoskeleton is crucial for the internal organization of eukaryotic cells. Several microtubule-associated proteins link microtubules to subcellular structures. A subclass of these proteins, the plus end–binding proteins (+TIPs), selectively binds to the growing plus ends of microtubules. Here, we reconstitute a vertebrate plus end tracking system composed of the most prominent +TIPs, end-binding protein 1 (EB1) and CLIP-170, in vitro and dissect their end-tracking mechanism. We find that EB1 autonomously recognizes specific binding sites present at growing microtubule ends. In contrast, CLIP-170 does not end-track by itself but requires EB1. CLIP-170 recognizes and turns over rapidly on composite binding sites constituted by end-accumulated EB1 and tyrosinated α-tubulin. In contrast to its fission yeast orthologue Tip1, dynamic end tracking of CLIP-170 does not require the activity of a molecular motor. Our results demonstrate evolutionary diversity of the plus end recognition mechanism of CLIP-170 family members, whereas the autonomous end-tracking mechanism of EB family members is conserved.     

Decimal Place Slope,A Fast and Precise Method for Quantifying 13C Incorporation Levels for Detecting the Metabolic Activity of Microbial Species

The metabolic incorporation of stable isotopes such as ¹³C or ¹⁵N into proteins has become a powerful tool for qualitative and quantitative proteome studies. We recently introduced a method that monitors heavy isotope incorporation into proteins and presented data revealing the metabolic activity of various species in a microbial consortium using this technique. To further develop our method using an liquid chromatography (LC)-mass spectrometry (MS)-based approach, we present here a novel approach for calculating the incorporation level of ¹³C into peptides by using the information given in the decimal places of peptide masses obtained by modern high-resolution MS. In the present study, the applicability of this approach is demonstrated using Pseudomonas putida ML2 proteins uniformly labeled via the consumption of [¹³C₆]benzene present in the medium at concentrations of 0, 10, 25, 50, and 100 atom %. The incorporation of ¹³C was calculated on the basis of several labeled peptides derived from one band on an SDS-PAGE gel. The accuracy of the calculated incorporation level depended upon the number of peptide masses included in the analysis, and it was observed that at least 100 peptide masses were required to reduce the deviation below 4 atom %. This accuracy was comparable with calculations of incorporation based on the isotope envelope. Furthermore, this method can be extended to the calculation of the labeling efficiency for a wide range of biomolecules, including RNA and DNA. The technique will therefore allow a highly accurate determination of the carbon flux in microbial consortia with a direct approach based solely on LC-MS.The metabolic incorporation of stable isotopes such as ¹³C or ¹⁵N into proteins has become a powerful component of qualitative and quantitative proteome studies (1). Incorporation of heavy isotopes can be used to analyze microbial processes such as turnover rates and also to help to establish structure-function relationships within microbial communities. Stable isotope probing (SIP¹) techniques based on DNA-SIP (2) and RNA-SIP (3) have been used for this purpose previously. With the introduction of protein-SIP (4), the need for an accurate alternative method for calculating label incorporation into biomolecules arose. Protein-SIP has several advantages compared with DNA/RNA-SIP, the most important being its capacity to detect dynamic levels of incorporation, whereas only labeled or unlabeled states can be categorized by means of DNA/RNA-SIP because of the need to separate ¹³C-DNA/RNA by density gradient centrifugation. Quantitative analysis of ¹³C incorporation levels is of the utmost importance, especially when unraveling carbon fluxes through either microbial communities or food webs with different trophic levels.In contrast to the incorporation of isotopically labeled amino acids, which is often used in quantitative proteomics (5), metabolic labeling by growth substrates and nutrients (e.g. salts) is often imperfect and makes the processing of mass spectrometry (MS) data difficult. For example, when the incorporation of ¹³C exceeds ∼2 atom %, common database search algorithms fail to identify peptides and proteins. The problem can only be managed successfully if a stable, known degree of ¹³C incorporation can be achieved during the experiment (6). Using a low labeling efficiency of roughly 5 atom %, Huttlin et al. (6) chose the altered envelope chain for calculating the incorporation and simultaneously used the signal intensity for a quantitative comparison with the sample that had a natural abundance of ¹³C. Database approaches for peptide identification can cope only with the natural abundance of carbon isotopes; they fail if the incorporation of ¹³C significantly exceeds the natural isotope abundance or if incorporation patterns occur in unpredictable ways (7).The simplest method for determining the incorporation level is to compare the unlabeled average mass of the monoisotopic peptide with the mass of the labeled protein, as estimated by matrix-assisted laser desorption/ionization or electrospray ionization MS (8, 9). A more advanced approach for determining the isotopic mass distribution of peptides is based on the isotopic distribution of the peaks of a peptide envelope (10, 11). Here, for a given isotopomer, the incorporation efficiency is defined as the percentage of incorporated ¹³C atoms with relation to the total number of carbon atoms with the natural isotope abundance (approximately 1.01 atom % ¹³C). As a reference, the theoretical isotopic distribution of a peptide is calculated based upon an algorithm described elsewhere (12). The isotope distribution of both unlabeled and labeled peptides can subsequently be used to calculate the incorporation level. For this method, an Excel spreadsheet (ProSIPQuant.xls) was developed (4). A similar approach, also based on the calculation of isotopic distributions, has been used in other studies (7). In these studies, however, the identification of the peptides is limited to those that have unlabeled counterparts; in addition, an exact calculation can be hampered by overlapping signals coming from additional peaks with similar masses.In the present study, we describe a new way of determining the isotope incorporation level. Our method makes use of characteristic patterns in the digits after the decimal point of the peptide masses generated by high-accuracy instruments such as the linear ion trap LTQ-Orbitrap (Thermo Fisher Scientific, Bremen, Germany). For tryptic peptides, typical regularities in the decimal places of the monoisotopic masses have been observed (13, 14). These observations have been explored in detail for theoretical and experimental data of proteins originating from Helicobacter pylori (15). As a result, a rule called the “half decimal place rule” (HDPR) was defined; it states that the decimal place is nearly half of the first digit for tryptic peptides with masses in the range of 500–1,000 Da. In other words, the exact mass of a peptide is equal to its nominal mass times ∼1.005. Because the difference between ¹²C and ¹³C is slightly greater than 1 Da, exactly 1.0033548378, the decimal places of a tryptic peptide''s mass are shifted in a regular manner by the incorporation level and lead to a significantly increased slope for the digits in the third and fourth place after the decimal point. This shift can be used to estimate the incorporation level of heavy isotopes into the protein. Detecting such shifts requires the highly accurate measurement possible with modern mass spectrometers such as the LTQ-Orbitrap, the Fourier transform ion cyclotron resonance, or the quadrupole time of flight. In this communication, we demonstrate the applicability of this approach using Pseudomonas putida ML2 proteins labeled uniformly via the consumption of [¹³C₆]benzene with five different substrate concentrations (0, 10, 25, 50, and 100 atom % of ¹³C). The ¹³C incorporation was calculated based on several labeled peptides derived from different proteins in one SDS-PAGE band. By these means, we have established a method that allows the determination of ¹³C incorporation into proteins and can be used to assess the metabolic activity of a given species within a mixed community.     

Determination of cell mass and polymyxin using multi-wavelength fluorescence

Multi-wavelength fluorescence was applied for on-line monitoring of cell mass and the antibiotic polymyxin B in Bacillus polymyxa cultivations. By varying the phosphate and nitrogen content of the medium different polymyxin-cell mass ratios could be obtained. Using this strategy, it was possible to investigate if multi-wavelength fluorescence is able to give independent prediction of the two parameters. Partial least square (PLS) regression was applied to establish mathematical relationships between off-line determined cell mass and polymyxin concentrations and on-line collected fluorescence data. For polymyxin one universal PLS model, with a correlation of 0.95 and a root mean square error of cross validation (RMSECV) of 35 mgl(-1), could be constructed. However, correlation between fluorescence and cell mass dry weight could not be established including data from all three types of cultivations. For data from each type of cultivation, separate models with high correlation and low RMSECV values were built. A large variation in cellular composition as a result of the different levels of nitrogen and phosphorus in the cultivations was the probable reason to the necessity of building three models. The results of the present investigation indicate that production of polymyxin is concomitantly regulated by phosphate and nitrogen as the highest polymyxin yield on cell mass, 0.17+/-0.01 gg(-1), was reached in the cultivations where both nitrogen and phosphate concentrations were kept low.     

The expanded octarepeat domain selectively binds prions and disrupts homomeric prion protein interactions

Insertion of additional octarepeats into the prion protein gene has been genetically linked to familial Creutzfeldt Jakob disease and hence to de novo generation of infectious prions. The pivotal event during prion formation is the conversion of the normal prion protein (PrPC) into the pathogenic conformer PrPSc, which subsequently induces further conversion in an autocatalytic manner. Apparently, an expanded octarepeat domain directs folding of PrP toward the PrPSc conformation and initiates a self-replicating conversion process. Here, based on three main observations, we have provided a model on how altered molecular interactions between wild-type and mutant PrP set the stage for familial Creutzfeldt Jakob disease with octarepeat insertions. First, we showed that wild-type octarepeat domains interact in a copper-dependent and reversible manner, a "copper switch." This interaction becomes irreversible upon domain expansion, possibly reflecting a loss of function. Second, expanded octarepeat domains of increasing length gradually form homogenous globular multimers of 11-21 nm in the absence of copper ions when expressed as soluble glutathione S-transferase fusion proteins. Third, octarepeat domain expansion causes a gain of function with at least 10 repeats selectively binding PrPSc in a denaturant-resistant complex in the absence of copper ions. Thus, the combination of both a loss and gain of function profoundly influences homomeric interaction behavior of PrP with an expanded octarepeat domain. A multimeric cluster of prion proteins carrying expanded octarepeat domains may therefore capture and incorporate spontaneously arising short-lived PrPSc-like conformers, thereby providing a matrix for their conversion.     

Local diversity of arable weeds increases with landscape complexity

Patterns of plant diversity are often related to local site conditions and to competitive interactions, but landscape context may also be important for local plant species richness. This is shown here by analysing the relationship between landscape complexity and local species richness of arable weeds in wheat fields. The fields were located in 18 landscapes characterised by a gradient in landscape complexity from structurally complex to structurally simple (39–94% arable land). We quantified local site conditions, field management intensity and landscape characteristics, and used principle component analyses to ordinate the environmental variables. The percentage of arable land was negatively correlated with perimeter–area ratio, habitat-type diversity and topographical heterogeneity, but landscape characteristics did not correlate with local site conditions and field management intensity. The number of plant species was mainly related to landscape characteristics and to a lesser extent to field management intensity (nitrogen fertilisation), whereas local soil characteristics did not contribute to the explanation of arable weed richness. In a geographic scale analysis using circular landscape sectors ranging from 1 km up to 5 km diameter, the predictive power of landscape complexity for local plant species richness was strongest at 2 km indicating a scale-dependent relationship between landscape context and plant species richness. Our results support the hypothesis that local plant species richness in arable fields is greatly influenced by processes operating at the landscape scale. Seed rain from ruderal source habitats and disturbed edges may be the most important underlying process.     

Methods to account for spatial autocorrelation in the analysis of species distributional data: a review

Species distributional or trait data based on range map (extent‐of‐occurrence) or atlas survey data often display spatial autocorrelation, i.e. locations close to each other exhibit more similar values than those further apart. If this pattern remains present in the residuals of a statistical model based on such data, one of the key assumptions of standard statistical analyses, that residuals are independent and identically distributed (i.i.d), is violated. The violation of the assumption of i.i.d. residuals may bias parameter estimates and can increase type I error rates (falsely rejecting the null hypothesis of no effect). While this is increasingly recognised by researchers analysing species distribution data, there is, to our knowledge, no comprehensive overview of the many available spatial statistical methods to take spatial autocorrelation into account in tests of statistical significance. Here, we describe six different statistical approaches to infer correlates of species’ distributions, for both presence/absence (binary response) and species abundance data (poisson or normally distributed response), while accounting for spatial autocorrelation in model residuals: autocovariate regression; spatial eigenvector mapping; generalised least squares; (conditional and simultaneous) autoregressive models and generalised estimating equations. A comprehensive comparison of the relative merits of these methods is beyond the scope of this paper. To demonstrate each method's implementation, however, we undertook preliminary tests based on simulated data. These preliminary tests verified that most of the spatial modeling techniques we examined showed good type I error control and precise parameter estimates, at least when confronted with simplistic simulated data containing spatial autocorrelation in the errors. However, we found that for presence/absence data the results and conclusions were very variable between the different methods. This is likely due to the low information content of binary maps. Also, in contrast with previous studies, we found that autocovariate methods consistently underestimated the effects of environmental controls of species distributions. Given their widespread use, in particular for the modelling of species presence/absence data (e.g. climate envelope models), we argue that this warrants further study and caution in their use. To aid other ecologists in making use of the methods described, code to implement them in freely available software is provided in an electronic appendix.     

Process‐based functions for seed retention on animals: a test of improved descriptions of dispersal using multiple data sets

Studies of external seed transport on animals usually assume that the probability of detachment is constant, so that seed retention should show a simple exponential relationship with time. This assumption has not been tested explicitly, and may lead to inaccurate representation of long distance seed dispersal by animals. We test the assumption by comparing the fit to empirical data of simple, two‐parameter functions. Fifty‐two data sets were obtained from five published studies, describing seed retention of 32 plant species on sheep, cattle, deer, goats and mice. Model selection suggested a simple exponential function was adequate for data sets in which seed retention was followed for short periods ( <48 h). The data gathered over longer periods (49–219 days) were best described by the power exponential function, a form of the stretched exponential which allows a changing dropping rate. In these cases the power exponential showed that seed dropping rate decreased with time, suggesting that seeds vary in attachment, with some seeds becoming deeply buried or wound up in the animal's coat. Comparison of fitted parameters across all the data sets also confirmed that seeds with adhesive structures have lower dropping rates than those without. We conclude that the seed dropping rate often changes with time during external transport on animals and that the power exponential is an effective function to describe this change. We advise that, to analyse seed dropping rates adequately, retention should be measured over reasonable time periods – until most seeds are dropped – and both the simple and power exponential functions should be fitted to the resulting data. To increase its utility, we provide functions describing the seed dropping rate and the dispersal kernel resulting from the power exponential relationship.     

Temporal variability of ecological niches: a study on intertidal macrobenthic fauna

The determination of temporal niche dynamics under field conditions is an important component of a species’ ecology. Recent developments in niche mapping, and the possibility to account for spatial autocorrelation in species distributions, hold promise for the statistical approach explored here. Using species counts from a landscape‐scale benthic monitoring programme in the western Dutch Wadden Sea during 1997–2005 in combination with sediment characteristics and tidal height as explanatory variables, we statistically derive realised niches for two bivalves, two crustaceans and three polychaetes, encompassing predators, suspension and bottom feeding functional groups. Unsurprisingly, realized niches varied considerably between species. Intraspecific temporal variation was assessed as overlap between the year‐specific niche and the overall mean niche, and this analysis revealed considerable variation between years. The main functional groups represented by these species showed idiosyncratic and wide variability through the study period. There were no strong associations between niche characteristics and mean abundance or body size. Our assessment of intraspecific niche variability has ramifications for species distribution models in general and offers advances from previous methods. 1) By assessing species’ realized niches in the multivariate environmental space, analyses are independent from the relative availability of particular environments. Predicted realized niches present differences between years, rather than annual differences in environmental conditions. 2) Using spatially explicit models to predict species habitat preferences provide more precise and unbiased estimates of species–environment relationships. 3) Current niche models assume constant niches, whereas we illustrate how much these can vary over only a few generations. This emphasizes the potentially limited scope of global change studies with forecasts based on single‐time species distribution snapshots.